Skip to main content

Full text of "Comparing the Microvascular Specificity of the 3- and 7-T BOLD Response Using ICA and Susceptibility-Weighted Imaging."

See other formats


HUMAN NEUROSCIENCE 



ORIGINAL RESEARCH ARTICLE 

published: 09 August 2013 
doi: 10.3389/fnhum. 2013. 00474 




Comparing the microvascular specificity of the 3- and 7-T 
BOLD response using ICA and susceptibility-weighted 
imaging 

Alexander Geililer 1 - 2 , Florian Ph. S. Fischmeister 1 - 2 , Giinther Grabner 2 - 3 , Moritz Wurnig 1 - 2 , Jakob Rath 1 - 2 , 
Thomas Foki 1 - 2 , Eva Matt 12 , Siegfried Trattnig 2 - 3 , Roland Beisteiner 12 and Simon Daniel Robinson 2 - 3 * 

' Study Group Clinical fMRI, Department of Neurology, Medical University of Vienna, Vienna, Austria 

2 High Field Magnetic Resonance Imaging Center of Excellence, Medical University of Vienna, Vienna, Austria 

3 Department of Biomedical Imaging and Image-guided Therapy, High Field Magnetic Resonance Imaging Center of Excellence, Medical University of Vienna, 
Vienna, Austria 



Edited by: 

Veronika Schopf Medical University 
of Vienna, Austria 

Reviewed by: 

Mark Haacke, Wayne State University, 
USA 

Andreas Deistung, Friedrich Schiller 
University Jena, Germany 

'Correspondence: 

Simon Daniel Robinson, Department 
of Biomedical Imaging and 
Image-guided Therapy, High Field 
Magnetic Resonance Center of 
Excellence, Medical University of 
Vienna, Lazarettgasse 14, A-1090 
Vienna, Austria 
e-mail: simon.robinson@ 
meduniwien.ac.at 



In functional MR I it is desirable for the blood-oxygenation level dependent (BOLD) signal to 
be localized to the tissue containing activated neurons rather than the veins draining that 
tissue. This study addresses the dependence of the specificity of the BOLD signal - the 
relative contribution of the BOLD signal arising from tissue compared to venous vessels - 
on magnetic field strength. To date, studies of specificity have been based on models or 
indirect measures of BOLD sensitivity such as signal to noise ratio and relaxation rates, and 
assessment has been made in isolated vein and tissue voxels. The consensus has been 
that ultra-high field systems not only significantly increase BOLD sensitivity but also speci- 
ficity, that is, there is a proportionately reduced signal contribution from draining veins. 
Specificity was not quantified in prior studies, however, due to the difficulty of establishing 
a reliable network of veins in the activated volume. In this study we use a map of venous 
vessel networks extracted from 7T high resolution Susceptibility-Weighted Images to quan- 
tify the relative contributions of micro- and macro-vasculature to functional MRI results 
obtained at 3 and 7T. High resolution measurements made here minimize the contribu- 
tion of physiological noise and Independent Component Analysis (ICA) is used to separate 
activation from technical, physiological, and motion artifacts. ICA also avoids the possibility 
of timing-dependent bias from different micro- and macro-vasculature responses. We find 
a significant increase in the number of activated voxels at 7T in both the veins and the 
microvasculature - a BOLD sensitivity increase - with the increase in the microvasculature 
being higher. However, the small increase in sensitivity at 7T was not significant. For the 
experimental conditions of this study, our findings do not support the hypothesis of an 
increased specificity of the BOLD response at ultra-high field. 

Keywords: fMRI, specificity, BOLD, susceptibility-weighted imaging, independent component analysis 



INTRODUCTION 

In functional MRI it is desirable for the blood-oxygenation level 
dependent (BOLD) signal to be localized, as closely as possi- 
ble, to the site of neurons activated by a task. Veins draining 
the capillary bed also give rise to BOLD signal changes, how- 
ever, leading to a shift in the detected signal away from its 
origins (Yacoub et al., 2001; Shmuel et al., 2007). The propor- 
tion of the BOLD response (quantified either by the number of 
activated voxels, or mean Z value) that arises in tissue to that 
which comes from the draining veins defines the specificity of 
the BOLD response. A body of evidence suggests that the rel- 
ative contribution of draining veins (Menon, 2012) decreases 
with field strength, leading to the expectation that in ultra-high 
field functional MRI (fMRI) the measured BOLD signal is bet- 
ter localized to its origin in gray matter (Gati et al., 1997; Ogawa 
et al, 1998; Yacoub et al, 2001; Duong et al, 2003). These stud- 
ies are based on numeric models, and measurements examining 
signal changes and relaxation rate changes in isolated veins and 



tissue voxels. To date, however, specificity has not been mea- 
sured with activation statistics or quantified over the whole acti- 
vated volume, due to difficulty in establishing a reliable network 
of veins. 

Questions as to the exact vascular origin of BOLD signal 
changes began to be raised soon after the first human fMRI exper- 
iments (Bandettini et al., 1992; Kwong et al., 1992; Ogawa et al., 
1992). In 1993, Gomiscek et al. indicated that inflow effects origi- 
nating in large vessels might be a relevant source of the fMRI signal 
(Gomiscek et al., 1993). Haacke et al. (1994) proceeded to demon- 
strate that the high signal changes observed in FLASH-based fMRI 
at 1.5 T were due to large vessels rather than the parenchyma. 
This finding was supported by experiments in which Stejskal- 
Tanner gradients, which suppress signal from flowing blood, were 
included in measurement sequences (Boxerman et al., 1995). The 
BOLD fMRI signal was reduced by 70-100%, demonstrating that 
the 1.5-TBOLD fMRI signal originates predominantly from blood 
in vessels rather than tissue. 



Frontiers in Human Neuroscience 



www.frontiersin.org 



August 2013 | Volume 7 | Article 474 | 1 



GeifJIer et al. 



BOLD specificity at 3 and 7T 



Later experiments across the field strengths 0.5, 1.5, and 4.0 T 
demonstrated that the percentage signal change between rest 
and activated conditions increases more than linearly with field 
strength in tissue but less than linearly in vessels (Gati et al., 
1997). These findings were extended to 7.0 T, and it was estab- 
lished that the short T2 of blood at high magnetic field was the 
origin of the reduced vessel contribution at very high field given 
the relatively long echo time used in fMRI (Yacoub et al., 2001). 
These studies provided evidence of and an explanation for an 
increase in the relative specificity of the BOLD signal to gray 
matter with field strength. The effect has only been measured 
in isolated vessels identified in Tl and T2 scans, however. The 
extent to which any changes in the relative contribution of the 
tissue and vessel signal affects the localization of BOLD signal in 
a bulk volume of activated tissue is clearly dependent on the dis- 
tribution of veins in the imaged volume, however. In this study 
we define maps of venous vessel networks from 7 T Susceptibility- 
Weighted Images (SWI) (Reichenbach et al, 1997, 1998) to allow 
the relative contributions of vessel and tissue signal to fMRI results 
obtained at 3 and 7 T to be quantified. Applying independent 
component analysis (ICA), rather than a General Linear Model 
analysis, allows a clean separation of activation from technical, 
physiological, and motion artifacts, and avoids the possibility of 
bias to draining vein or microvasculature responses which could 
have different timing and thereby influence the assessment of 
specificity. 

MATERIALS AND METHODS 
HEALTHY SUBJECTS 

Twelve healthy, right handed volunteers (eight male, four female, 
mean age 31.6 years, age range from 23 to 45) participated in the 
study, which was approved by the Ethics Committee of the Medical 
University of Vienna, with written informed consent. 

TASK DESIGN AND PROCEDURE 

A hand motor task was chosen because it elicits a strong and repro- 
ducible BOLD response which is localized in a well circumscribed 
region. Volunteers were instructed to perform repetitive opening 
and closing of the right hand at 1 Hz. Auditory start and stop 
commands were computer-generated and communicated via the 
scanner intercom system. The sequence of timed commands was 
executed with the software Presentation (Neurobehavioral Sys- 
tems, Albany, CA, USA) and was triggered by the MRI scanner. 
All subjects performed a simple blocked design consisting of four 
movement and five rest periods of 20 s each, with two runs at each 
field strength. 

DATA ACQUISITION 

All subjects were examined with both a 3-T Siemens MAGNETOM 
TIM TRIO scanner and a 7-T Siemens MAGNETOM scanner 
(Siemens Medical, Erlangen, Germany). A 32 channel head coil 
was used on both systems (on 3 T, manufactured by Siemens Med- 
ical, on 7 T, manufactured by Nova Medical, Wilmington, MA, 
USA). 

Functional data were acquired on both systems with high reso- 
lution 2D single shot gradient-echo (GE) EPI, with slices aligned 
parallel to the AC-PC plane and whole brain coverage. To ensure 



that results obtained here are relevant to fMRI in general prac- 
tice, we chose to assess specificity using the echo time which, 
for each field strength, provides the maximum BOLD sensitiv- 
ity (approximately equal to T2* in gray matter; Deichmann et al., 
2002). Protocols used at 3 and 7T were also independently opti- 
mized according to specific absorption rate (SAR) constraints, the 
requirement of whole brain coverage and other recommendations 
in the literature (Triantafyllou et al., 2005; Robinson et al., 2008; 
Speck et al, 2008; van der Zwaag et al., 2009). 

At both field strengths, GE-EPI was acquired with a square field 
of view (FOV) of 220 mm, in-plane matrix size 220 x 220, with 
slice thickness of 2 mm and 20% gap (i.e., 1 mm x 1 mm x 2.4 mm 
voxels), with 73 repetitions, a repetition time (TR) of 3000 ms, 
fat suppression with a chemical shift selective saturation pulse 
prior to every slice, 6/8 partial Fourier factor (omitting the first 
25% of k-space phase-encoding lines), and parallel imaging with 
a GRAPPA-iPAT factor of 4. This relatively high GRAPPA fac- 
tor was required to achieve the desired echo times with these 
high resolution acquisitions. At 3 T, 37 slices were acquired with 
TE = 35ms, a receiver bandwidth per pixel (BW) of 1082 Hz, 
flip angle (FA) of 90°. At 7 T, 44 slices were acquired with 
TE = 22ms, BW = 990Hz, FA =75°. As these echo times are 
different between the two field strengths (35 ms for 3 T, 22 ms 
for 7T) we also performed an additional comparison of speci- 
ficity with a single subject (subject 8) using runs with both 
echo times - 35 and 22 ms - at both field strengths, to assess 
to what extent specificity findings are echo-time dependent. A 
total of four additional motor runs - two with 35 ms and two 
with 22 ms - were measured at each field strength for subject 
8 only. 

High resolution, fully flow compensated T2* -weighted 3D GE 
images were acquired at 7T for SWI. The acquisition matrix 
size was 704 x 704 x 96 voxel, with a FOV of 220 mm, lead- 
ing to 0.3125 mm x 0.3125 mm x 1.2 mm, TE/TR= 11.9/28 ms, 
FA =15°, with BW=163Hz/px, and an acquisition time of 
13 min 20 s. 

DATA PROCESSING 
Functional data analysis 

Echo planar images were motion corrected using MCFLIRT (Jenk- 
inson et al., 2002) from Version 5.0.1 of the FSL software package 
(Smith et al., 2004), with all volumes registered to the first vol- 
ume of the first functional experiment (3 and 7T separately). 
ICA was performed in this native EPI space with "MELODIC" 
(Beckmann and Smith, 2004) for each subject with no smooth- 
ing applied. MELODIC was run in multi-session tensorial mode 
(TICA) without skull stripping but with the brain volumes of 
interest (VOI) as a confinement. The mean bias-corrected EPI was 
used as a background image for functional overlays. The thresh- 
old for the mixture model-based inference was 0.5 (the default) 
and the model order, or number of components into which 
the data is split - was determined automatically using Laplacian 
estimation. 

To identify which voxels overlay veins and which tissue, func- 
tional data were registered to SWI space in a number of linear 
registration steps, with increasing number of degrees of free- 
dom as the quality of the result improved, followed by non-linear 



Frontiers in Human Neuroscience 



www.frontiersin.org 



August 2013 | Volume 7 | Article 474 | 2 



GeifSler et al. 



BOLD specificity at 3 and 7T 



SWI MASK 




Veins Tissue 




FIGURE 1 | Generation of the venous vessel maps (A) SWI (B) Brain 
mask. (C) Preliminary vein mask, achieved by applying threshold "T" to 
SWI. (D) Smoothed version of preliminary vein mask. (E) Final vein mask 
derived from (D). (F) Tissue mask derived from (D). 



registration. Registration was performed using the mean EPI and 
magnitude SWI image, both of which were skull stripped with 
BET2 (Jenkinson et al, 2002), with subject-specific fractional 
intensity thresholds and bias-corrected with FSL's "FAST" package. 
Magnitude SWI were additionally denoised with FSL's non-linear 
noise reduction tool "SUSAN" and intensity-normalized to a value 
of 1000. A binary brain mask was generated from this image (by 
setting all non-zero values to 1). This was for used with FNIRT 
and for the generation of venous vessel maps (see "Generation of 
Venous Vessel Maps"). 

For 7 T EPI, the first step was linear registration of mean skull 
stripped, bias-corrected EPI to 7 T SWI using FSL's "FLIRT" (Jenk- 
inson et al., 2002) with correlation ratio as the cost function and 
7 degrees of freedom (three translational, three rotational, and 
global rescaling). The output matrix of this transformation was 
used as a starting point for a second execution of FLIRT (again 
to SWI), using mutual information as the cost function and 12 
degrees of freedom. The final registration step was a non-linear 
transformation of the output of the linear transformations to 
SWI using "FNIRT." In the light of the sequence-dependent inten- 
sity disparity between EPI and SWI, the local non-linear intensity 
model was used for FNIRT. 

For 3 T EPI, registration steps were as described for 7 T above, 
other than that they were preceded by the addition step of linear 
registration to the 7-T EPI using FLIRT with 7 degrees of freedom. 
The normalized correlation ratio was used as the cost function 
for all linear registration stages of 3 T data. Normalized correla- 
tion ratio is usually used for intermodal registration, but provided 
the best results in this application due to the contrast differences 
between 3 and 7 T data. The global non-linear intensity model was 
used for FNIRT. 

This multi-step registration procedure was found to provide 
accurate registration for all subjects. For both 3 and 7T fMRI 
data all transformation steps, both linear and non-linear, were 
combined to define the transformation from EPI to 7 T SWI. The 
merged transformations were finally applied to ICA maps. This 
approach ensured the equal treatment of 3 and 7T functional 
data - of a single transformation with one resampling step, vital 
because every applied transformation causes some smoothing of 
the data. 

Generation of venous vessel maps 

Vessel maps were generated semi-automatically from SWI magni- 
tude images using MATLAB (Math Works, Inc., Natick, MA, USA). 
Steps are illustrated in Figure 1, and were as follows. For each sub- 
ject's magnitude SWI (Figure 1 A), a threshold "T" was determined 
for the whole volume by hand, below which images were classi- 
fied as consisting of veins or background signal. Voxels whose 
value was below T and which were inside a BET mask of the 
brain (Figure IB) were set to 1, and all other voxels were set to 
0 (Figure 1C). This preliminary vein mask was smoothed using 
the "smoothn" MATLAB function with the smoothing parameter 
S of 1 (Garcia, 2009) (Figure ID). A binary vein map was created 
by assigning the value of 1 to voxels in the smoothed prelimi- 
nary mask (Figure ID) which exceeded a value of 0.3 (yielding 
Figure IE). The vein mask was compared by visual inspection 
with the SWI for the verisimilitude of the vessels identified, and the 



threshold "T" modified, if necessary. A "tissue" mask was assigned 
the value of one where values in the smoothed preliminary mask 
(Figure ID) were below 0.15 (yielding Figure IF). 

The process of thresholding, smoothing, and thresholding a 
second time removed isolated voxels and bridged gaps in ves- 
sels (compare Figures 1C,E). Using different thresholds for the 
vein and tissue masks (0.15 and 0.3 respectively) led to a cleaner 
allocation of voxels to the vein and tissue categories. fMRI acti- 
vation was classified as being in a vein if it coincided with the 
vein mask and being in the microvasculature if it was in the tissue 
mask. Voxels in the zone between the two were not considered in 
further analysis. 

Statistical analysis 

To assess effects related to the two field strengths under con- 
trolled conditions, anatomical VOIs were located within the pri- 
mary hand motor area. VOI's were manually defined for each 
subject by an experienced fMRI expert (RB) and comprised the 



Frontiers in Human Neuroscience 



www.frontiersin.org 



August 2013 | Volume 7 | Article 474 | 3 



GeifJIer et al. 



BOLD specificity at 3 and 7T 



neurophysiological representation of the human hand area, i.e., 
the knob structure. The individual VOI was the same for both 
field strengths. The following values were calculated for both veins 
and microvasculature: (1) number of activated voxels (alterna- 
tive hypothesis test at a Gaussian mixture modeling threshold 
of p > 0.5; Beckmann and Smith, 2004), (2) percent activated 
voxels, (3) mean Z value, and (4) ratio of the mean Z values 
in the microvasculature/veins for all voxels within the anatom- 
ical VOI. Both the number of activated voxels as well as the 
mean f-values of those voxels was assessed with a Student's paired 
two-tailed f-test carried out in Microsoft Excel 2007 (Redmond, 
Washington, MA, USA). A decreased vascular contribution to 
the BOLD signal is to be expected at 7T due to the short T2 
of blood. As specificity effects can be expected to be echo-time 
dependent, data at the same echo times (22 and 35 ms) were 
likewise compared. 

RESULTS 

Figure 2 illustrates, for a single subject, both the accuracy of the 
image registration and the appearance of the vein maps, the outline 
of which are overlaid (in cyan) on sample 3 and 7 T EPI volumes, 
and SWI. Some identified veins appear to lie outside the brain. 
A proportion of these are genuine veins on the surface of the 
brain, beyond the cortical surface, which appear further outside 
the brain due to partial volume effect over slices. In EPI there 
is also strong T2* dephasing of signal from the periosteal and 
meningeal dural layers, which makes the brain appears slightly 
smaller than the skull-stripped SWI, enhancing the impression 
that these veins lie further outside the brain. Any errors in the 
vein maps outside the anatomically defined VOIs in the primary 
motor cortex do not affect our results, as analysis was constrained 
to those VOIs. 

Figure 3 illustrates typical functional results within the prede- 
fined anatomical VOI (green) for a single subject. Row A shows 
voxels above threshold (determined via the Gaussian mixture 
modeling approach described in the see "Statistical Analysis"), 
row B all functional voxels. The zoomed depiction clarifies the 




FIGURE 2 | Verification of the accuracy of the normalization 7T — SWI 
space and 3 — 7T (SWI) space for a typical subject and illustration of 
the corresponding vein map. For the illustration only, the boundaries of 
the veins (rather than the vein masks themselves) are shown, overlaid in 
cyan. These were generated with the contour function of CorelDraw (Corel 
Corporation, Ottawa, ON, Canada). Bottom row: zoomed depiction of the 
hand area. 



situation inside the target area. Both the number of voxels above 
threshold as well as the mean Z values of all voxels in the VOI were 
assessed for each subject (see Table 1). 

On average, 21% more voxels were above threshold in veins 
at 7 T than at 3 T and 42% more voxels were above threshold 
in the microvasculature at 7T than at 3T (see Table 2). These 
increases in BOLD sensitivity with field strength in both veins 
and the microvasculature were statistically significant in student's 
two-tailed t -tests assessed at p < 0.05. The proportion of activated 
voxels in the microvasculature to the total did not differ signif- 
icantly between the two field strengths, however, indicating no 
increase in specificity. 

Mean Z values were significantly higher in the 7-T results in 
both the vessels and the microvasculature. In veins, the increase 
was 41%, in the microvasculature it was 48%. The increase in the 
ratio of mean Z values in the microvasculature to veins with field 
strength was small and not statistically significant, indicating no 
increase in specificity. The same finding, of no substantial increase 
in specificity, held when the assessment was carried out at the same 
echo time (Table 3). 

DISCUSSION 

The field strength dependence of the specificity of the BOLD 
response has been studied using high resolution fMRI at 3 and 
7 T with a hand task. ICA was used to identify task-related acti- 
vation in order to obviate possible bias of a model-based analysis 
to either the vascular or microvascular response, as these could 
be subject to different latencies. Activation maps were meticu- 
lously normalized to the space of vessel maps derived from high 
resolution 7 T SWI scans using state-of-the-art non-linear image 
registration. The results of this analysis allowed both the relative 
sensitivity and the relative specificity of the BOLD response at 3 
and 7 T to be assessed. 

There was significant increase in the number of activated vox- 
els at 7 T in both the veins and the microvasculature, with the 
increase in the microvasculature being higher. The increase in 




FIGURE 3 | Exemplified single subject illustration (radiological 
convention). (A) Functional image with vessels (cyan) thresholded IC map 
and anatomical VOI (green) overlaid. (B) As in (A), but with no thresholding 
applied to IC map. A zoomed representation of the target area is also 
illustrated. 



Frontiers in Human Neuroscience 



www.frontiersin.org 



August 2013 | Volume 7 | Article 474 | 4 



GeiBler et al. 



BOLD specificity at 3 and 7T 



Table 1 | Individual subject results showing the number of activated voxels and mean Z values in regions identified as vessels and 
microvasculature. 

3T Subject Number of activated voxels in VOI Mean Z over all voxel in VOI 

Vessels ixvasc % In \ivasc Vessel iivasc Z (iv/Z vessel 



1 


1521 


17,367 


91.9 


2.1 


1.3 


0.61 


2 


1323 


6982 


84.1 


2.6 


1.1 


0.41 


3 


1345 


8058 


85.7 


2.2 


1.0 


0.48 


4 


1209 


5056 


80.7 


1.9 


1.0 


0.53 


5 


1926 


4720 


71.0 


2.1 


0.7 


0.33 


6 


2339 


8856 


79.1 


2.8 


1.4 


0.50 


7 


1314 


6746 


83.7 


1.9 


0.8 


0.44 


8 


2083 


8097 


79.5 


3.3 


1.2 


0.37 


9 


1655 


7075 


81.0 


1.8 


0.8 


0.44 


10 


2124 


13,940 


86.8 


3.6 


1.5 


0.41 


11 


1026 


7732 


88.3 


1.8 


1.0 


0.54 


12 


2094 


14,617 


875 


2.2 


1.0 


0.45 


Mean 


1670 


9100 


82.9 


2.4 


1.1 


0.45 


SD 


430 


4000 


7.5 


0.6 


0.2 


0.08 


1 


1986 


25,895 


68.0 


2.8 


1.8 


0.64 


2 


1366 


9849 


92.9 


3.3 


1.8 


0.53 


3 


1840 


10,869 


87.8 


3.4 


1.7 


0.50 


4 


1269 


5598 


85.5 


2.3 


1.4 


0.59 


5 


2422 


11,218 


81.5 


3.0 


1.5 


0.49 


6 


2392 


11,206 


82.2 


2.9 


1.6 


0.56 


7 


1664 


9019 


82.4 


2.3 


1.0 


0.45 


8 


2343 


8490 


84.4 


3.4 


1.3 


0.38 


9 


1804 


5316 


78.4 


2.3 


0.9 


0.41 


10 


2339 


20,568 


74.7 


4.9 


2.0 


0.40 


11 


2213 


16,217 


89.8 


3.9 


1.8 


0.45 


12 


2780 


19,780 


88.0 


4.5 


1.8 


0.40 


Mean 


2030 


12,800 


84.3 


3.2 


1.5 


0.47 


SD 


460 


6400 


5.6 


0.8 


0.3 


0.08 


f-Test 


0.0025* 


0.0025* 


n.s. 


0.0017* 


7.33E-05* 


n.s. 



All values are investigated within neuroanatomicallydefinedVOIs. 'Indicate statistically significant differences between 3 and 7X and "n.s.," indicates a non-significant 
result. 



Table 2 | Summary sensitivity and specificity results extracted from 
Table 1. 



7/3T SENSITIVITY 




W vessels 


1.21 (0.31) 


N iivasc 


1.42 (0.44) 


Z vessels 


1.41 (0.35) 


Z [ivasc 


1.48 (0.32) 


7/3T SPECIFICITY 




% In [ivasc 


1.01 (0.13) 


Z nv/Z vessel 


1.07 (0.17) 



Values in brackets are standard deviations on the mean. 



both tissue classes confirms the increase in BOLD sensitivity of 
7T fMRI observed in other studies (Triantafyllou et al., 2005; 
van der Zwaag et al, 2009; Beisteiner et al, 2011). While the 



fact that there was a larger increase in the number of voxels 
activated in the microvasculature might suggest an increase in 
microvascular specificity at 7 T, this tendency was non-significant 
due to high variance over subjects. Findings were the same for 
mean Z values. There were significant increases, of ~40%, in 
Z values in both the veins and tissue in 7T results compared 
to 3 T results. Again, the increase was consistently higher in tis- 
sue, but not significantly so. The obvious conclusion, that BOLD 
specificity is not significantly higher at 7 T than at 3 T could 
be affected by our choice of echo times, which was different 
(and near optimum) for each field strength (35 ms for 3 T and 
22 ms for 7T) (Yacoub et al, 2001; Robinson et al, 2004). We 
tested the generality of our conclusion, however, by perform- 
ing additional measurements at both 22 and 35 ms at each field 
strength. Although there was a small difference in all Z values 
between echo times the ratio Z |xvasc/Z vessel did not differ sub- 
stantially. We therefore conclude that while changing echo-time 



Frontiers in Human Neuroscience 



www.frontiersin.org 



August 2013 | Volume 7 | Article 474 | 5 



GeifJIer et al. 



BOLD specificity at 3 and 7T 



Table 3 | A comparison of functional specificities measured at two the same echo times at 3 and 7T in one subject (subject 8; mean over two 
runs). 

3T Echo time (ms) Number of activated voxels in VOI Mean Z over all voxel in VOI 

Vessels iivasc Vessel nvasc Ratio 

Iivasc % In iivasc Z mean Z mean Z |iv/Z vessel 

22 1735 10,608 85.94 2.55 1.41 0.55 

35 2028 13,004 86.51 2.88 1.63 0.57 

7T 22 3561 28,622 88.94 8.15 3.38 0.41 

35 3630 31,608 89.70 7.81 3.71 0.48 



All values are Investigated within neuroanatomically defined VOIs. There is no substantial increase in specificity, measured via percentage of activated voxels in the 
microvasculature, or the ratio of mean Z values in the microvasculature to that in vessels, with field strength, even if the same echo times are used at 3 and 71 



(unsurprisingly) influences BOLD sensitivity, it did not, to a 
measurable degree, affect specificity. 

Our primary hypothesis in this study was that the increase 
in sensitivity would be larger in tissue than veins, demonstrating 
an increase in specificity and indicating that improved localiza- 
tion of the BOLD signal is to be expected at ultra-high field. 
This could not be confirmed, in apparent contradiction of prior 
work. For instances, Gati et al. (1997) predicted larger signal 
increases in tissue than veins in the visual cortex at 0.5, 1.5, and 
4.0 T on the basis of measurement of SNR, AR2*, and R2* at 
these field strengths. Yacoub et al. (2001) extended Gati et al.'s 
findings relating to AR2* and R2* from 4 to 7T, but likewise 
based their predictions on increasing signal changes compared 
to relatively constant noise. The authors assumed that thermal 
noise "will ultimately dominate the noise term" - i.e., that when 
physiological noise is better understood and imaging systems are 
more developed, physiological noise will be reduced to below the 
level of thermal noise. Despite progress on this front (e.g., Boya- 
cioglu and Barth, 2012), this point has not yet been reached. One 
source of physiological noise is pulsatory blood flow. In conven- 
tional EPI at least, the signal changes related to pulsatory flow 
(which typically have a frequency of 1-1.5 Hz) are undersam- 
pled with TRs of ~0.5 Hz, and other physiological noise sources 
cannot be comprehensively removed, meaning that physiological 
noise is still at least as large as thermal noise at 7 T (Triantafyl- 
lou et al, 2005). The relatively high resolution measurements 
we made here with full brain coverage and accelerated imag- 
ing go as far as possible to reducing the relative contribution 
of physiological noise to the total (physiological plus thermal 
noise), and thereby yield the best specificity possible. A relatively 
high GRAPPA factor of 4 was used to achieve the desired echo 
times with these high resolution acquisitions. While the use of 
high parallel imaging factors increases g-factor noise and reduces 
BOLD sensitivity, the BOLD sensitivity in this study was suffi- 
cient to detect activation in the primary motor cortices of all 
subject. This GRAPPA factor is not expected to have any influence 
on specificity. 

Fully automatic identification of vessels from SWI scans is a 
complex process and the subject of considerable research effort 
as a separate field (see, e.g., Frangi et al, 1998). Our attempts to 
apply a leading existing approach (Kroon, 2009) in this study led 



to imperfect detection of vessels and false positive vessel detection 
and enlarged vessels, and motivated the development of our own 
method. While our simple magnitude threshold-based approach 
performed much better than existing methods with this data it 
is also subject to shortcomings. Firstly, a threshold needs to be 
set to determine where veins end (how broad a vein mask is 
for a given appearance in SWI) and where tissue begins. Our 
vein maps were defined quite conservatively and a margin was 
left before defining voxels as belonging to tissue. In this way, 
we minimized the influence of this border zone between vein 
and tissue on our specificity results. Veins have low signal in 
SWI, but so does CSF and the interhemispheric fissure, so these 
regions are erroneously included in the vein mask. These errors 
did not influence results obtained here, as all analysis was per- 
formed within a VOI for the primary motor cortex which excluded 
these problems, but would lead to errors if applied uncritically in 
other studies. 

It should be noted that our finding of no demonstrable increase 
in specificity at 7 T compared to 3 T are constrained to the motor 
system. A motor paradigm was chosen due to its robustness and 
our group's interest in precise localization of motor function in 
the context of presurgical planning (Beisteiner et al., 1995, 2001). 
Future work could involve extending this examination to the visual 
system, which would afford more direct comparison with prior 
studies, although specificity findings should be independent of the 
region studied. Finally, these findings are constrained to the mea- 
surement sequence and methods applied at both field strengths. 
Future developments in fast imaging may allow both the sensitiv- 
ity and specificity of ultra-high field fMRI to be increased (Poser 
etal.,2013). 

In summary, this study, comparing high resolution fMRI of the 
motor system at 3 and 7 T, does not confirm a significant increase 
in the specificity of the BOLD response at ultra-high field. 

ACKNOWLEDGMENTS 

This study was carried out as part of the "Vienna Advanced 
Clinical Imaging Center" (VIACLIC) project, funded by the 
Vienna Spots of Excellence Program of the Center of Innova- 
tion and Technology, City of Vienna (ZIT), Austria. Additional 
support was provided by the Austrian Science Fund (FWF) project 
KLI264. 



Frontiers in Human Neuroscience 



www.frontiersin.org 



August 2013 | Volume 7 | Article 474 | 6 



Geiftler et al 



BOLD specificity at 3 and 7T 



REFERENCES 

Bandettini, P. A., Wong, E. C, Hinks, 
R. S., Tikofsky, R. S., and Hyde, J. S. 
(1992). Time course EPI of human 
brain function during task activa- 
tion. Magn. Reson. Med. 25, 390-397. 
doi:10.1002/mrm.l910250220 

Beckmann, C. E, and Smith, S. M. 
(2004). Probabilistic independent 
component analysis for functional 
magnetic resonance imaging. IEEE 
Trans. Med. Imaging 23, 137-152. 
doi:10.1109/TMI.2003.822821 

Beisteiner, R., Gomiscek, G., Erdler, 
M., Teichtmeister, C, Moser, E., 
and Deecke, L. (1995). Comparing 
localization of conventional func- 
tional magnetic resonance imaging 
and magnetoencephalography. 
Eur. }. Neurosci. 7, 1121-1124. 
doi:10.1111/j.l460-9568.1995. 
tbO 1101.x 

Beisteiner, R., Robinson, S., Wurnig, 
M., Hilbert, M., Merksa, K., Rath, 
J., et al. (2011). Clinical fMRI: evi- 
dence for a 7T benefit over 3T. Neu- 
roimage 57, 1015-1021. doi:10.1016/ 
j.neuroimage. 201 1.05.010 

Beisteiner, R., Windischberger, C, 
Lanzenberger, R., Edward, V., Cun- 
nington, R., Erdler, M., et al. (2001). 
Finger somatotopy in human motor 
cortex. Neuroimage 13, 1016-1026. 
doi:10.1006/nimg.2000.0737 

Boxerman, J. L., Bandettini, P. A., 
Kwong, K. K., Baker, J. R., Davis, T. 
L., Rosen, B. R., et al. (1995). The 
intravascular contribution to fMRI 
signal change: Monte Carlo model- 
ing and diffusion-weighted studies 
in vivo. Magn. Reson. Med. 34, 4-10. 
doi: 10.1 002/mrm. 1 9 1 0340 1 03 

Boyacioglu, R., and Barth, M. (2012). 
Generalized iNverse imaging (GIN): 
ultrafast fMRI with physiological 
noise correction. Magn. Reson. Med. 
doi:10.1002/mrm.24528 

Deichmann, R., Josephs, O., Hut- 
ton, C, Corfield, D. R., and 
Turner, R. (2002). Compensation of 
susceptibility-induced BOLD sensi- 
tivity losses in echo-planar fMRI 
imaging. Neuroimage 15, 120-135. 
doi:10.1006/nimg.2001.0985 

Duong, T. Q., Yacoub, E., Adriany, 
G., Hu, X., Ugurbil, K., and Kim, 
S. G. (2003). Microvascular BOLD 
contribution at 4 and 7 T in the 
human brain: gradient-echo and 
spin-echo fMRI with suppression of 
blood effects. Magn. Reson. Med. 49, 
1019-1027. doi:10.1002/mrm.l0472 



Frangi, A. E, Niessen, W. J., Vincken, 
K. L., and Viergever, M. A. (1998). 
"Multiscale vessel enhancement fil- 
tering" in Medical Image Comput- 
ing and Computer-Assisted Medical 
Intervention -MICCAr98,Vol. 1496 
in Lecture Notes in Computer Sci- 
ence, eds W. M. Wells, A. Colchester, 
and S. Delp (Berlin: Springer), 130- 
137. 

Garcia, D. (2009). Robust Spline 
Smoothing for 1-D to N-D Data. 
MATLAB Central. Available at: 
http://www.mathworks.com/matlab 
central/fileexchange/25634-robust- 
spline-smoothing-for-l-d-to-n-d- 
data [accessed]. 

Gati, J. S., Menon, R. S., Ugur- 
bil, K., and Rutt, B. K. (1997). 
Experimental determination of the 
BOLD field strength dependence 
in vessels and tissue. Magn. Reson. 
Med. 38, 296-302. doi: 10. 1002/ 
mrm.1910380220 

Gomiscek, G., Beisteiner, R., Hittmair, 
K., Muller, E., and Moser, E. (1993). 
A possible role of in-flow effects in 
functional MR-imaging. MAGMA 1, 
109-113. doi:10.1007/BF01769410 

Haacke, E. M., Hopkins, A., Lai, S., 
Buckley, P., Friedman, L., Meltzer, 
H., et al. (1994). 2D and 3D high 
resolution gradient echo functional 
imaging of the brain: venous con- 
tributions to signal in motor cor- 
tex studies. NMR Biomed. 7, 54-62. 
doi:10.1002/nbm.l940070109 

Jenkinson, M., Bannister, P., Brady, 
M., and Smith, S. (2002). Improved 
optimization for the robust and 
accurate linear registration and 
motion correction of brain images. 
Neuroimage 17, 825-841. doi:10. 
1006/nimg.2002.1132 

Kroon, D. J. (2009). Hessian 
Based Frangi Vesselness Fil- 
ter. MATLAB Central. Avail- 
able at: http://www.mathworks. 
com/matlabcentral/fileexchange/ 
24409-hessian-based-frangi-vessel 
ness-filter [accessed]. 

Kwong, K. K., Belliveau, J. W., Chesler, 
D. A., Goldberg, I. E., Weisskoff, R. 
M., Poncelet, B. P., et al. (1992). 
Dynamic magnetic resonance imag- 
ing of human brain activity dur- 
ing primary sensory stimulation. 
Proc. Natl. Acad. Sci. U.S.A. 89, 
5675-5679. doi:10.1073/pnas.89.12. 
5675 

Menon, R. S. (2012). The great brain 
versus vein debate. Neuroimage 62, 



970-974. doi: 10. 1 0 1 6/j .neuroimage. 
2011.09.005 

Ogawa, S., Menon, R. S., Kim, S. G., 
and Ugurbil, K. (1998). On the char- 
acteristics of functional magnetic 
resonance imaging of the brain. 
Annu. Rev. Biophys. Biomol. Struct. 
27, 447-474. doi:10.1146/annurev. 
biophys.27.1.447 

Ogawa, S., Tank, D. W., Menon, R., Eller- 
mann, J. M., Kim, S. G., Merkle, H., 
et al. { 1 992 ) . Intrinsic signal changes 
accompanying sensory stimulation: 
functional brain mapping with mag- 
netic resonance imaging. Proc. Natl. 
Acad. Sci U.S.A. 89, 5951-5955. doi: 
10.1073/pnas.89. 13.5951 

Poser, B. A., Barth, M., Goa, P. E., 
Deng, W, and Stenger, V. A. (2013). 
Single-shot echo-planar imaging 
with Nyquist ghost compensation: 
interleaved dual echo with acceler- 
ation (IDEA) echo-planar imaging 
(EPI). Magn. Reson. Med. 69, 37-47. 
doi:10.1002/mrm.24222 

Reichenbach, J., Venkatesan, R., 
Schillinger, D., and Haacke, E. 
(1997). Small vessels in the human 
brain: MR-venography with deoxy- 
hemoglobin as an intrinsic contrast 
agent. Radiology 204, 272-277. 

Reichenbach, J. R., Essig, M., Haacke, 
E. M., Lee, B. C, Przetak, C, 
Kaiser, W. A., et al. (1998). High- 
resolution venography of the brain 
using magnetic resonance imag- 
ing. MAGMA 6, 62-69. doi:10.1016/ 
S1352-8661(98)00011-8 

Robinson, S., Pripfl, J., Bauer, H., and 
Moser, E. (2008). The impact of 
EPI voxel size on SNR and BOLD 
sensitivity in the anterior medio- 
temporal lobe: a comparative group 
study of deactivation of the default 
mode. MAGMA 21,279-290. doi:10. 
1007/sl0334- 008-0128-0 

Robinson, S., Windischberger, C, 
Rauscher, A., and Moser, E. (2004). 
Optimized 3T EPI of the amygdalae. 
Neuroimage 22, 203-210. doi:10. 
1016/j.neuroimage.2003. 12.048 

Shmuel, A., Yacoub, E., Chaimow, D., 
Logothetis, N. K., and Ugurbil, 
K. (2007). Spatio-temporal point- 
spread function of fMRI signal in 
human gray matter at 7 Tesla. Neu- 
roimage 35, 539-552. doi: 10.1016/j. 
neuroimage.2006. 1 2.030 

Smith, S. M., Jenkinson, M., Woolrich, 
M. W., Beckmann, C. E, Behrens, 
T. E., Johansen-Berg, H., et al. 
(2004). Advances in functional and 



structural MR image analysis and 
implementation as FSL. Neuroim- 
age 23, S208-S219. doi:10.1016/j. 
neuroimage. 2004.07. 05 1 

Speck, O., Stadler, J., and Zaitsev, M. 
(2008). High resolution single-shot 
EPI at 7T. MAGMA 21, 73-86. doi: 
10.1007/sl0334-007-0087-x 

Triantafyllou, C, Hoge, R. D., Krueger, 
G., Wiggins, C. J., Potthast, A., Wig- 
gins, G. C, et al. (2005). Compari- 
son of physiological noise at 1.5 T, 
3 T and 7 T and optimization of 
fMRI acquisition parameters. Neu- 
roimage 26, 243-250. doi:10.1016/j. 
neuroimage.2005.01 .007 

van der Zwaag, W., Francis, S., Head, K., 
Peters, A., Gowland, P., Morris, P., et 
al. (2009). fMRI at 1.5, 3 and 7 T: 
characterising BOLD signal changes. 
Neuroimage 47, 1425-1434. doi:10. 
1016/j.neuroimage.2009.05.015 

Yacoub, E., Shmuel, A., Pfeuffer, J., 
Van De Moortele, P. E, Adriany, G., 
Andersen, P., et al. (2001). Imaging 
brain function in humans at 7 Tesla. 
Magn. Reson. Med. 45, 588-594. doi: 
10.1002/mrm.l080 

Conflict of Interest Statement: The 

authors declare that the research was 
conducted in the absence of any com- 
mercial or financial relationships that 
could be construed as a potential con- 
flict of interest. 

Received: 17 February 2013; accepted: 29 
July 2013; published online: 09 August 
2013. 

Citation: Geifiler A, Fischmeister FPhS, 
Grabner G, Wurnig M, Rath J, Foki 
T, Matt E, Trattnig S, Beisteiner R 
and Robinson SD (2013) Compar- 
ing the microvascular specificity of the 
3- and 7-T BOLD response using 
ICA and susceptibility-weighted imag- 
ing. Front. Hum. Neurosci. 7:474. doi: 
10.3389/fnhum.2013.00474 
Copyright © 2013 Geifiler, Fischmeis- 
ter, Grabner, Wurnig, Rath, Foki, Matt, 
Trattnig, Beisteiner and Robinson. Thisis 
an open-access article distributed under 
the terms of the Creative Commons 
Attribution License (CC BY). The use, 
distribution or reproduction in other 
forums is permitted, provided the origi- 
nal author(s) or licensor are credited and 
that the original publication in this jour- 
nal is cited, in accordance with accepted 
academic practice. No use, distribution or 
reproduction is permitted which does not 
comply with these terms. 



Frontiers in Human Neuroscience 



www.frontiersin.org 



August 2013 | Volume 7 | Article 474 | 7